Incorporating Intra-flow Dependencies and Inter-flow Correlations for Traffic Matrix Prediction

Abstract

Traffic matrix (TM) prediction is essential for effective traffic engineering and network management. Based on our analysis of real traffic traces from Wide Area Network, the traffic flows in TM are both time-varying (i.e. with intra-flow dependencies) and correlated with each other (i.e. with inter-flow correlations). However, most existing works in TM prediction ignore inter-flow correlations. In this paper, we propose a novel Attention-based Convolutional Recurrent Neural Network (ACRNN) model to capture both intra-flow dependencies and inter-flow correlations. ACRNN mainly contains two components: 1) Correlational Modeling employs attention-based convolutional structures to capture the correlation of any two flows in TMs; 2) Temporal Modeling uses attention-based recurrent structures to model the long-term temporal dependencies of each flow, and then predicts TMs according inter-flow correlations and intra-flow dependencies. Experiments on two real-world datasets show that, when predicting the next TM, ACRNN model reduces the Mean Squared Error by up to 44.8% and reduces the Mean Absolute Error by up to 30.6%, compared to state-of-the-art method; and the gap is even larger when predicting the next multiple TMs. Besides, simulation results demonstrate that ACRNN's accurate prediction can help traffic engineering to mitigate traffic congestion.